{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from IPython.display import Image\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Pandas - Series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "结构化数据分析利器（依赖numpy）\n",
    "\n",
    "提供了多种高级数据结构\n",
    "\n",
    "拥有大数据索引和处理能力\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Pandas中的主要数据对象是Series和DataFrame。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Series\n",
    "* 创建series\n",
    "* 改变dtype类型\n",
    "* Series性质\n",
    "* 通过index排序\n",
    "* 数据选择"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Series类似一维数组对象，包含一个数组数据和一个与数组关联的数据标签，被叫做索引 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename='series.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## 创建 Series\n",
    "* 通过list\n",
    "* 通过dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "### 通过list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    a\n",
       "1    b\n",
       "2    c\n",
       "3    d\n",
       "4    e\n",
       "5    f\n",
       "6    g\n",
       "dtype: object"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(['a','b','c','d','e','f','g'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    a\n",
       "2    b\n",
       "3    c\n",
       "4    d\n",
       "5    e\n",
       "6    f\n",
       "7    g\n",
       "dtype: object"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(['a','b','c','d','e','f','g'],index=[1,2,3,4,5,6,7])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4, 5, 6, 7]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(range(1,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    a\n",
       "2    b\n",
       "3    c\n",
       "4    d\n",
       "5    e\n",
       "6    f\n",
       "7    g\n",
       "dtype: object"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(['a','b','c','d','e','f','g'],index=list(range(1,8)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    a\n",
       "2    b\n",
       "3    c\n",
       "4    d\n",
       "5    e\n",
       "6    f\n",
       "7    g\n",
       "Name: NAME, dtype: object"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(['a','b','c','d','e','f','g'],index=list(range(1,8)),name='NAME')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "2    3\n",
       "3    4\n",
       "dtype: object"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series([1,2,3,4],dtype='str')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 通过dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    a\n",
       "2    b\n",
       "3    c\n",
       "4    d\n",
       "5    e\n",
       "6    f\n",
       "7    g\n",
       "Name: NAME, dtype: object"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series({1:'a',2:'b',3:'c',4:'d',5:'e',6:'f',7:'g'},name='NAME')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## 转变dtype类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "2    3\n",
       "3    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s=pd.Series([1,2,3,4])\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    2.0\n",
       "2    3.0\n",
       "3    4.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.astype('float')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "2    3\n",
       "3    4\n",
       "dtype: int32"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "2    3\n",
       "3    4\n",
       "dtype: object"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.astype('str')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Series 性质"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(s.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=4, step=1)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(s.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4,)"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4]"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 1, 1: 2, 2: 3, 3: 4}"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 课后练习"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "用两种方法创建一个学生成绩的series，student a 86分，student b 90分，student c 75分，student d 100分"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "产生包括所有学生的list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "产生所有分数的list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## 通过index排序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5    a\n",
       "1    b\n",
       "3    c\n",
       "8    d\n",
       "dtype: object"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s=pd.Series(['a','b','c','d'],index=[5,1,3,8])\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    b\n",
       "3    c\n",
       "5    a\n",
       "8    d\n",
       "dtype: object"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8    d\n",
       "5    a\n",
       "3    c\n",
       "1    b\n",
       "dtype: object"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.sort_index(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5    a\n",
       "1    b\n",
       "3    c\n",
       "8    d\n",
       "dtype: object"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "s.sort_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    b\n",
       "3    c\n",
       "5    a\n",
       "8    d\n",
       "dtype: object"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5    a\n",
       "1    b\n",
       "3    c\n",
       "8    d\n",
       "dtype: object"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8    d\n",
       "3    c\n",
       "1    b\n",
       "5    a\n",
       "dtype: object"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    b\n",
       "3    c\n",
       "5    a\n",
       "8    d\n",
       "dtype: object"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5    a\n",
       "1    b\n",
       "3    c\n",
       "8    d\n",
       "dtype: object"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.sort_values(inplace=True)\n",
    "s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Series选择\n",
    "* 通过位置选择\n",
    "* 通过index选择\n",
    "* 通过条件选择"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### 通过位置选择"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5    a\n",
       "1    b\n",
       "3    c\n",
       "8    d\n",
       "dtype: object"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'a'"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5    a\n",
       "1    b\n",
       "3    c\n",
       "dtype: object"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.iloc[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5    a\n",
       "3    c\n",
       "8    d\n",
       "dtype: object"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.iloc[[0,2,3]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5    a\n",
       "3    c\n",
       "8    d\n",
       "dtype: object"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.iloc[[0,-2,-1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### 通过index选择"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5    a\n",
       "1    b\n",
       "3    c\n",
       "8    d\n",
       "dtype: object"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'a'"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.loc[5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5    a\n",
       "8    d\n",
       "dtype: object"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.loc[[5,8]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'a'"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5    a\n",
       "8    d\n",
       "dtype: object"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[[5,8]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 通过条件选择"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5    a\n",
       "1    b\n",
       "3    c\n",
       "8    d\n",
       "dtype: object"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5    False\n",
       "1    False\n",
       "3     True\n",
       "8     True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s>'b'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    c\n",
       "8    d\n",
       "dtype: object"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[s>'b']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    c\n",
       "8    d\n",
       "dtype: object"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.loc[s>'b']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5    False\n",
       "1    False\n",
       "3     True\n",
       "8    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(s>'b')& (s<'d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    c\n",
       "dtype: object"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.loc[(s>'b')& (s<'d')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5    False\n",
       "1    False\n",
       "3     True\n",
       "8    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.gt('b')& s.lt('d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    c\n",
       "dtype: object"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.loc[s.gt('b')& s.lt('d')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 课后练习"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "创建一个学生成绩的series，student a 86分，student b 90分，student c 75分，student d 100分"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "选出student c的成绩"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "选出分数高于85分的学生"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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